标题： 非参数敏感性分析用于混合控制试验中的偏差范围界定 显示英文标题

标题： A Non-Parametric Sensitivity Analysis for Bounding Bias in Hybrid Control Trials

摘要： 在数字时代，比以往任何时候都更容易收集和利用试验中的丰富协变量信息。 近期的研究探讨了如何利用这些信息来整合外部对照，包括使用混合对照试验（HCT），其中随机对照试验与外部对照相结合。 由于HCT能够在保持部分随机化的同时提高试验效率，因此特别引人关注。 然而，大多数HCT估计器依赖于一个不现实的假设：即外部对照来自与试验受试者相同的人群（可能是在协变量条件下的）。 关于违反这一假设所引入的不可避免偏差的量化工作很少，这减缓了HCT设计的接受度。 为了解决这个问题，我们引入了一种非参数敏感性分析，该分析认识到这一假设可以重新表述为“无未观察到的混杂因素”的假设。 我们利用遗漏变量偏差的方法来估计未测量协变量引入的最大偏差，从而对可能使重要发现无效的因果差距进行关键评估。 我们证明，即使对协变量-结果关系和试验受试者与外部受试者的可区分性有相对薄弱的理解，这种方法也能可靠地限制偏差，同时允许效率的提升。 最后，我们讨论了设计和评估HCT时需要考虑的问题，结合了模拟和理论分析的见解。 显示英文摘要

摘要： In the digital era, it is easier than ever to collect and exploit rich covariate information in trials. Recent work explores how to use this information to integrate external controls, including the use of hybrid control trials (HCTs) where a randomized controlled trial is augmented with external controls. HCTs are of particular interest due to their ability to preserve partial randomization while also improving trial efficiency. However, most HCT estimators rely on an unrealistic assumption: that the external controls are drawn from the same population as the trial subjects (perhaps conditionally on covariates). There has been little formal work to quantify the inevitable bias introduced from a violation of this assumption, slowing the acceptance of HCT designs. To address this, we introduce a non-parametric sensitivity analysis that recognizes that the assumption can be reframed as a "no unobserved confounders" assumption. We leverage omitted variable bias methodologies to estimate the maximum bias introduced from unmeasured covariates, allowing for a critical evaluation of the causal gap that can invalidate significant findings. We show that with a relatively weak understanding of the covariate-outcome relationship and the distinguishability of trial and external subjects, this method reliably bounds bias while also allowing for gains in efficiency. We conclude by discussing considerations for designing and evaluating HCTs, drawing on insights from simulations and theoretical analyses.